{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、找到持仓增多的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始连接数据库...\n",
      "开始查找上次数据...\n",
      "开始导出数据...\n",
      "导出数据到【csv/持有基金个数增多的股票2022-11-14.csv】成功，谢谢使用。\n",
      "导出数据到【csv/基金持有市值增多的股票2022-11-14.csv】成功，谢谢使用。\n",
      "                                            持有基金个数增多的股票                                             \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2022-10-14】持有基金个数</th>\n",
       "      <th>【2022-11-14】持有基金个数</th>\n",
       "      <th>【2022-10-14】基金持有市值</th>\n",
       "      <th>【2022-11-14】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>长江电力</td>\n",
       "      <td>91</td>\n",
       "      <td>172</td>\n",
       "      <td>5.616642</td>\n",
       "      <td>13.837559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>阳光电源</td>\n",
       "      <td>99</td>\n",
       "      <td>174</td>\n",
       "      <td>38.786281</td>\n",
       "      <td>57.839538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>迈瑞医疗</td>\n",
       "      <td>173</td>\n",
       "      <td>236</td>\n",
       "      <td>57.168116</td>\n",
       "      <td>62.799188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>德业股份</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>8.351405</td>\n",
       "      <td>17.847747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国平安</td>\n",
       "      <td>198</td>\n",
       "      <td>253</td>\n",
       "      <td>45.870583</td>\n",
       "      <td>49.353537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>西部超导</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>5.160265</td>\n",
       "      <td>9.367817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>亨通光电</td>\n",
       "      <td>12</td>\n",
       "      <td>58</td>\n",
       "      <td>4.343418</td>\n",
       "      <td>10.870692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>亿纬锂能</td>\n",
       "      <td>150</td>\n",
       "      <td>193</td>\n",
       "      <td>86.333877</td>\n",
       "      <td>74.777375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>格力电器</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "      <td>6.781547</td>\n",
       "      <td>12.895326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>北方华创</td>\n",
       "      <td>79</td>\n",
       "      <td>119</td>\n",
       "      <td>10.624077</td>\n",
       "      <td>25.957384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   股票名称  【2022-10-14】持有基金个数  【2022-11-14】持有基金个数  【2022-10-14】基金持有市值  \\\n",
       "0  长江电力                  91                 172            5.616642   \n",
       "1  阳光电源                  99                 174           38.786281   \n",
       "2  迈瑞医疗                 173                 236           57.168116   \n",
       "3  德业股份                  29                  91            8.351405   \n",
       "4  中国平安                 198                 253           45.870583   \n",
       "5  西部超导                  26                  78            5.160265   \n",
       "6  亨通光电                  12                  58            4.343418   \n",
       "7  亿纬锂能                 150                 193           86.333877   \n",
       "8  格力电器                  58                 100            6.781547   \n",
       "9  北方华创                  79                 119           10.624077   \n",
       "\n",
       "   【2022-11-14】基金持有市值  \n",
       "0           13.837559  \n",
       "1           57.839538  \n",
       "2           62.799188  \n",
       "3           17.847747  \n",
       "4           49.353537  \n",
       "5            9.367817  \n",
       "6           10.870692  \n",
       "7           74.777375  \n",
       "8           12.895326  \n",
       "9           25.957384  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            基金持有市值增多的股票                                             \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2022-10-14】持有基金个数</th>\n",
       "      <th>【2022-11-14】持有基金个数</th>\n",
       "      <th>【2022-10-14】基金持有市值</th>\n",
       "      <th>【2022-11-14】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>山煤国际</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>6.704920</td>\n",
       "      <td>17.546339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>长城汽车</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>9.282010</td>\n",
       "      <td>12.934608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中航重机</td>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "      <td>11.791613</td>\n",
       "      <td>14.999670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中国海油</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>1.129016</td>\n",
       "      <td>3.888024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>扬杰科技</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>3.181720</td>\n",
       "      <td>5.592410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>青岛啤酒股份</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.877044</td>\n",
       "      <td>3.038346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>四维图新</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>3.945918</td>\n",
       "      <td>4.810429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>建发股份</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>3.397155</td>\n",
       "      <td>3.941144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>天地科技</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0.126037</td>\n",
       "      <td>0.648468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>海康威视</td>\n",
       "      <td>119</td>\n",
       "      <td>96</td>\n",
       "      <td>17.361801</td>\n",
       "      <td>17.789724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     股票名称  【2022-10-14】持有基金个数  【2022-11-14】持有基金个数  【2022-10-14】基金持有市值  \\\n",
       "0    山煤国际                  22                  22            6.704920   \n",
       "1    长城汽车                  22                  12            9.282010   \n",
       "2    中航重机                  50                  46           11.791613   \n",
       "3    中国海油                  16                  14            1.129016   \n",
       "4    扬杰科技                  14                  14            3.181720   \n",
       "5  青岛啤酒股份                  16                  16            0.877044   \n",
       "6    四维图新                  24                  20            3.945918   \n",
       "7    建发股份                  26                  21            3.397155   \n",
       "8    天地科技                  12                  10            0.126037   \n",
       "9    海康威视                 119                  96           17.361801   \n",
       "\n",
       "   【2022-11-14】基金持有市值  \n",
       "0           17.546339  \n",
       "1           12.934608  \n",
       "2           14.999670  \n",
       "3            3.888024  \n",
       "4            5.592410  \n",
       "5            3.038346  \n",
       "6            4.810429  \n",
       "7            3.941144  \n",
       "8            0.648468  \n",
       "9           17.789724  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           持有基金个数减少最多的股票                                            \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2022-10-14】持有基金个数</th>\n",
       "      <th>【2022-11-14】持有基金个数</th>\n",
       "      <th>【2022-10-14】基金持有市值</th>\n",
       "      <th>【2022-11-14】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>药明康德</td>\n",
       "      <td>229</td>\n",
       "      <td>140</td>\n",
       "      <td>117.866911</td>\n",
       "      <td>75.297284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>赣锋锂业</td>\n",
       "      <td>125</td>\n",
       "      <td>65</td>\n",
       "      <td>72.483337</td>\n",
       "      <td>34.439016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东方财富</td>\n",
       "      <td>268</td>\n",
       "      <td>208</td>\n",
       "      <td>62.941651</td>\n",
       "      <td>37.937644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>明阳智能</td>\n",
       "      <td>96</td>\n",
       "      <td>36</td>\n",
       "      <td>6.961093</td>\n",
       "      <td>1.088892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德方纳米</td>\n",
       "      <td>94</td>\n",
       "      <td>37</td>\n",
       "      <td>10.082721</td>\n",
       "      <td>2.673228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>博腾股份</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>27.731580</td>\n",
       "      <td>6.963886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>通威股份</td>\n",
       "      <td>178</td>\n",
       "      <td>135</td>\n",
       "      <td>68.042028</td>\n",
       "      <td>33.279227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>中信证券</td>\n",
       "      <td>129</td>\n",
       "      <td>91</td>\n",
       "      <td>34.426537</td>\n",
       "      <td>26.607692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>华友钴业</td>\n",
       "      <td>118</td>\n",
       "      <td>80</td>\n",
       "      <td>90.895162</td>\n",
       "      <td>52.514529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>天赐材料</td>\n",
       "      <td>90</td>\n",
       "      <td>53</td>\n",
       "      <td>39.111486</td>\n",
       "      <td>21.975669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   股票名称  【2022-10-14】持有基金个数  【2022-11-14】持有基金个数  【2022-10-14】基金持有市值  \\\n",
       "0  药明康德                 229                 140          117.866911   \n",
       "1  赣锋锂业                 125                  65           72.483337   \n",
       "2  东方财富                 268                 208           62.941651   \n",
       "3  明阳智能                  96                  36            6.961093   \n",
       "4  德方纳米                  94                  37           10.082721   \n",
       "5  博腾股份                  62                  18           27.731580   \n",
       "6  通威股份                 178                 135           68.042028   \n",
       "7  中信证券                 129                  91           34.426537   \n",
       "8  华友钴业                 118                  80           90.895162   \n",
       "9  天赐材料                  90                  53           39.111486   \n",
       "\n",
       "   【2022-11-14】基金持有市值  \n",
       "0           75.297284  \n",
       "1           34.439016  \n",
       "2           37.937644  \n",
       "3            1.088892  \n",
       "4            2.673228  \n",
       "5            6.963886  \n",
       "6           33.279227  \n",
       "7           26.607692  \n",
       "8           52.514529  \n",
       "9           21.975669  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "from dataModel import JJs, Stocks, db\n",
    "\n",
    "old_date = \"2022-10-14\"\n",
    "new_date = \"2022-11-14\"\n",
    "\n",
    "db.close()\n",
    "print(\"开始连接数据库...\")\n",
    "db.connect()\n",
    "sSelect = Stocks.select()\n",
    "print(\"开始查找上次数据...\")\n",
    "olds = sSelect.where((Stocks.date == old_date))\n",
    "aups = []\n",
    "bups = []\n",
    "downs = []\n",
    "yesterday = int(new_date.split(\"-\")[-1]) - 1\n",
    "yesterday = \"-\".join(new_date.split(\"-\")[:-1]) + f\"-{yesterday}\"\n",
    "for old in olds:\n",
    "    name = old.name\n",
    "    new = sSelect.where(\n",
    "        (Stocks.date == new_date or Stocks.date == yesterday),\n",
    "        (Stocks.name == name),\n",
    "        (Stocks.n >= 10),\n",
    "    )\n",
    "    if new:\n",
    "        new = new[0]\n",
    "        data = {\n",
    "            \"股票名称\": new.name,\n",
    "            f\"【{old_date}】持有基金个数\": old.n,\n",
    "            f\"【{new_date}】持有基金个数\": new.n,\n",
    "            f\"【{old_date}】基金持有市值\": old.money,\n",
    "            f\"【{new_date}】基金持有市值\": new.money,\n",
    "        }\n",
    "        if new.n > old.n:\n",
    "            aups.append(data)\n",
    "        elif round(new.money, 3) > round(old.money, 3):\n",
    "            bups.append(data)\n",
    "        else:\n",
    "            downs.append(data)\n",
    "\n",
    "\n",
    "def asort(stock):\n",
    "    \"\"\"获取排序的key，持有基金个数按数量绝对值差排序\"\"\"\n",
    "    return stock[f\"【{new_date}】持有基金个数\"] - stock[f\"【{old_date}】持有基金个数\"]\n",
    "\n",
    "\n",
    "def bsort(stock):\n",
    "    \"\"\"获取排序的key，基金持仓市值按比例排序\"\"\"\n",
    "    return stock[f\"【{new_date}】基金持有市值\"] - stock[f\"【{old_date}】基金持有市值\"]\n",
    "\n",
    "\n",
    "aups = sorted(aups, key=asort, reverse=True)\n",
    "bups = sorted(bups, key=bsort, reverse=True)\n",
    "downs = sorted(downs, key=asort)\n",
    "dfa = pd.DataFrame(aups)\n",
    "dfb = pd.DataFrame(bups)\n",
    "dfc = pd.DataFrame(downs)\n",
    "\n",
    "print(\"开始导出数据...\")\n",
    "patha = f\"csv/持有基金个数增多的股票{new_date}.csv\"\n",
    "dfa.to_csv(patha, encoding=\"utf-8\")\n",
    "print(f\"导出数据到【{patha}】成功，谢谢使用。\")\n",
    "pathb = f\"csv/基金持有市值增多的股票{new_date}.csv\"\n",
    "dfb.to_csv(pathb, encoding=\"utf-8\")\n",
    "print(f\"导出数据到【{pathb}】成功，谢谢使用。\")\n",
    "db.close()\n",
    "if len(dfa):\n",
    "    print(f\"{'持有基金个数增多的股票':^100}\")\n",
    "    display(dfa[:10])\n",
    "else:\n",
    "    print(f\"{'好惨，没有基金持有个数增多的股票':^100}\")\n",
    "if len(dfb):\n",
    "    print(f\"{'基金持有市值增多的股票':^100}\")\n",
    "    display(dfb[:10])\n",
    "else:\n",
    "    print(f\"{'好惨，没有基金持有市值增多的股票':^100}\")\n",
    "print(f\"{'持有基金个数减少最多的股票':^100}\")\n",
    "display(dfc[:10])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、绘制股票的曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始查找股票数据...\n",
      "数据查找完毕，开始绘图...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "from dataModel import JJs, Stocks, db\n",
    "\n",
    "#import matplotlib    \n",
    "#print(matplotlib.matplotlib_fname())# 获取mpl目录\n",
    "\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['CangErJinKai01-9128-W05-6','KaiTi','SimHei' , 'FangSong']  # 汉字字体,优先使用楷体，如果找不到楷体，则使用黑体\n",
    "plt.rcParams['font.size'] = 12  # 字体大小\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号\n",
    "\n",
    "\n",
    "name=\"闻泰科技\"\n",
    "\n",
    "sSelect = Stocks.select()\n",
    "print(\"开始查找股票数据...\")\n",
    "dates=[]\n",
    "datas = sSelect.where((name == Stocks.name))\n",
    "datas_dict={\n",
    "    '持有基金个数':[],\n",
    "    '持有基金市值':[],\n",
    "}\n",
    "for stock in datas:\n",
    "    stock:Stocks\n",
    "    dates.append(pd.to_datetime(stock.date))\n",
    "    datas_dict['持有基金个数'].append(stock.n)\n",
    "    datas_dict['持有基金市值'].append(stock.money)\n",
    "\n",
    "df = pd.DataFrame(datas_dict)\n",
    "df.index=dates\n",
    "print(\"数据查找完毕，开始绘图...\")\n",
    "\n",
    "fig = sns.lineplot(data=df)\n",
    "fig.get_figure().set_figwidth(12)  # 设置宽度\n",
    "fig.get_figure().set_figheight(8)  # 设置高度\n",
    "plt.title(f\"{name}基金持仓变化\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
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